Literature DB >> 21527097

Learning in a virtual environment using haptic systems for movement re-education: can this medium be used for remodeling other behaviors and actions?

Alma S Merians1, Gerard G Fluet, Qinyin Qiu, Ian Lafond, Sergei V Adamovich.   

Abstract

Robotic systems that are interfaced with virtual reality gaming and task simulations are increasingly being developed to provide repetitive intensive practice to promote increased compliance and facilitate better outcomes in rehabilitation post-stroke. A major development in the use of virtual environments (VEs) has been to incorporate tactile information and interaction forces into what was previously an essentially visual experience. Robots of varying complexity are being interfaced with more traditional virtual presentations to provide haptic feedback that enriches the sensory experience and adds physical task parameters. This provides forces that produce biomechanical and neuromuscular interactions with the VE that approximate real-world movement more accurately than visual-only VEs, simulating the weight and force found in upper extremity tasks. The purpose of this article is to present an overview of several systems that are commercially available for ambulation training and for training movement of the upper extremity. We will also report on the system that we have developed (NJIT-RAVR system) that incorporates motivating and challenging haptic feedback effects into VE simulations to facilitate motor recovery of the upper extremity post-stroke. The NJIT-RAVR system trains both the upper arm and the hand. The robotic arm acts as an interface between the participants and the VEs, enabling multiplanar movements against gravity in a three-dimensional workspace. The ultimate question is whether this medium can provide a motivating, challenging, gaming experience with dramatically decreased physical difficulty levels, which would allow for participation by an obese person and facilitate greater adherence to exercise regimes.
© 2011 Diabetes Technology Society.

Entities:  

Mesh:

Year:  2011        PMID: 21527097      PMCID: PMC3125920          DOI: 10.1177/193229681100500215

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  28 in total

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2.  Integrated versus isolated training of the hemiparetic upper extremity in haptically rendered virtual environments.

Authors:  Qinyin Qiu; Gerard G Fluet; Soha Saleh; Ian Lafond; Alma S Merians; Sergei V Adamovich
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2010

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4.  Enhanced gait-related improvements after therapist- versus robotic-assisted locomotor training in subjects with chronic stroke: a randomized controlled study.

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Journal:  Stroke       Date:  2008-05-08       Impact factor: 7.914

5.  Incorporating haptic effects into three-dimensional virtual environments to train the hemiparetic upper extremity.

Authors:  Sergei V Adamovich; Gerard G Fluet; Alma S Merians; Abraham Mathai; Qinyin Qiu
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2009-08-07       Impact factor: 3.802

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Review 9.  Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review.

Authors:  Gert Kwakkel; Boudewijn J Kollen; Hermano I Krebs
Journal:  Neurorehabil Neural Repair       Date:  2007-09-17       Impact factor: 3.919

10.  Minimal detectable change and clinically important difference of the Wolf Motor Function Test in stroke patients.

Authors:  Keh-chung Lin; Yu-wei Hsieh; Ching-yi Wu; Chia-ling Chen; Yuh Jang; Jung-sen Liu
Journal:  Neurorehabil Neural Repair       Date:  2009-03-16       Impact factor: 3.919

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  6 in total

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Authors:  Bryan A Rabin; Grigore C Burdea; Doru T Roll; Jasdeep S Hundal; Frank Damiani; Simcha Pollack
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2.  Virtual reality technologies for research and education in obesity and diabetes: research needs and opportunities.

Authors:  Abby G Ershow; Charles M Peterson; William T Riley; Albert Skip Rizzo; Brian Wansink
Journal:  J Diabetes Sci Technol       Date:  2011-03-01

3.  Visuomotor discordance during visually-guided hand movement in virtual reality modulates sensorimotor cortical activity in healthy and hemiparetic subjects.

Authors:  Eugene Tunik; Soha Saleh; Sergei V Adamovich
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4.  Reinforced feedback in virtual environment for rehabilitation of upper extremity dysfunction after stroke: preliminary data from a randomized controlled trial.

Authors:  Paweł Kiper; Michela Agostini; Carlos Luque-Moreno; Paolo Tonin; Andrea Turolla
Journal:  Biomed Res Int       Date:  2014-03-13       Impact factor: 3.411

5.  Auditory and visual cueing modulate cycling speed of older adults and persons with Parkinson's disease in a Virtual Cycling (V-Cycle) system.

Authors:  Rosemary Gallagher; Harish Damodaran; William G Werner; Wendy Powell; Judith E Deutsch
Journal:  J Neuroeng Rehabil       Date:  2016-08-19       Impact factor: 4.262

6.  Using an interactive virtual environment to integrate a digital Action Research Arm Test, motor imagery and action observation to assess and improve upper limb motor function in patients with neuromuscular impairments: a usability and feasibility study protocol.

Authors:  Frank Behrendt; Corina Schuster-Amft
Journal:  BMJ Open       Date:  2018-07-16       Impact factor: 2.692

  6 in total

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